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6,410 results for “Results”
Meningitis hGWAS results (summary statistics)
<p>Summary statistics for association between genetic variation and meningitis phenotypes. Contains human genome association and interaction effects (pGWAS.tar.bz2).</p> <p>Unpack with `tar xf `. Contents are described in the README.</p>
Results from retrospective Baltic Sea biodiversity indicator status assessment using BEAT 3.0 tool
<p>Here we present all our results from retrospective Baltic Sea biodiversity indicator data analysis using BEAT 3.0. The BEAT tool (Nygård et al. 2018) is an R coded software (Murray & Nygård 2018, available online: <a href="https://zenodo.org/record/1288315#.XRxNp2cXYg4">https://zenodo.org/record/1288315#.XRxNp2cXYg4</a>) developed for analyzing marine biodivesity status. It follows the strucuture of EU's Marine Strategy Framework Directive. For more detailed metadata about the tool, see Nygård et al. 2018.</p> <p>We used data from various biodiversity indicators in two areas of the Baltic Sea: Bothnian Sea and Gulf of Finland. BEAT integrates indicators to ecosystem components and aggregates them spatially (more details can be found in Nygård et al. 2018). We produced retrospective time series of integrated and aggregated indicators. The yearly assessments are done using the moving average of the indicator status of the past 5 years in order to gain a more robust assessment result. The assessment follows the protocol of biodiversity assessment in the HELCOM Holistic assessment <a href="http://www.helcom.fi/baltic-sea-trends/holistic-assessments">http://www.helcom.fi/baltic-sea-trends/holistic-assessments</a></p> <p>In the results table, all different spatial levels as well as ecosystem components are shown. All indicator results have a value between 0 and 1. If the indicator has a value over 0.6, it is considered to be in a good environmental status. Below are short description of the different columns:</p> <ul> <li>SAUID: ID of the spatial assessment unit (SAU) used. The largest SAU is Baltic Sea with an ID 1. It is divided to smaller SAUs and all individual SAUs have their own ID.</li> <li>SAUlevel: The highest possible level of SAU is the Baltic Sea and it is the level 1. The sea basins (for example Bothnian Sea) are the level 2 and so on.</li> <li>ECID: Ecosystem component ID. All possible indicators have their own ID. See the list of ecosystem components in the input files of the tool (Murray & Nygård 2018).</li> <li>EClevel: Ecosystem component level. The level 1 is biodiversity, in the level 2 it is divided to pelagic habitat, birds, fish, benthic habitat and mammals and so on.</li> <li>EcosystemComponent: this column tells the ecosystem component. It can be higher level e.g. biodversity or an individual indicator for certain taxa.</li> <li>EQR: ecological quality ratio. The status of the certain ecosystem component in a certain SAU. The value varies between 0 and 1. If it is over 0.6, the ecosystem component is considered to be in a good status.</li> <li>Columns H-T: These refer to certain descriptors of Marine Strategy Framework Directive. If the ecosystem component is considered to have a link to a certain descriptor, an EQR value is given.</li> <li>year: year of the assessment. Note: all the yearly values are a moving average of past 5 years.</li> </ul> <p> </p>
DS3_DITOs_Capacity_Building_Tools_Results-events-database
<p>This csv file is the dataset of events that were completed by the DITOs consortium during the 3 year H2020 Coordination and Support Action 1/6/16-31/5/19</p> <p>It contains the following fields:</p> <p>Partner - The consortium partner responsible for organising the event</p> <p>Title - The name of the event</p> <p>Name of event as described in the DoA - The type of the event as listed in the DoA, or the word 'additional' if the event was not envisaged in the DoA</p> <p>Page link - the link to the together science.eu page that held the event detail</p> <p>Status - the status of the event (planned, completed or cancelled)</p> <p>Date - the start date of the event in YYY-MM-DD format</p> <p>Time - the start time of the event in 24HH:MM format</p> <p>End date - the end date of the event in YYY-MM-DD format</p> <p>End time - the end time of the event in 24HH:MM format</p> <p>The event type - conference, exhibition, gaming competition, online, travelling bus or workshop</p> <p>Audience number - an estimate (from the hosting partner) of the number of attendees</p> <p>%Female - an estimate (from the hosting partner) of the percentage of attendees</p> <p>Workpackage - WP1-6 - the work package from the DoA relevant to that event</p> <p>Partner org name and facilitator - the partner to contact and name(s) of facilitators</p> <p>Lower age bracket - an estimate of the age of the youngest attendee</p> <p>Upper age bracket - an estimate of the age of the oldest attendee</p> <p>URLs - any associated websites for outputs or publicity</p> <p>Event ID - a unique event identifier of the format XXXX_YYYYMMDD(z) where XXXX is partner identifier (ECSA, eutema, UCL, UPD, RBINS, Tekiu, UNIGE, WS, meritum, KI, MP - as defined in the Grant agreement), YYYYMMDD is the start date of the event and z is an optional suffix (a through z) used if the partner ran more than one event on that date</p> <p>Location - the address where the event took place</p> <p>Reporting period - the Grant agreement reporting period the event relates to</p> <p>Phase - the DoA phase the event relates to</p> <p>NGO - A list of any NGOs involved in co-hosting / contributing to the event</p> <p>DIY and local communities - A list of any DIY and local communities s involved in co-hosting / contributing to the event</p> <p>Local and national government - A list of any government bodies involved in co-hosting / contributing to the event</p> <p>Industry, company and start-ups - A list of any industry/start-ups involved in co-hosting / contributing to the event</p> <p>Other - A list of any other organisations involved in co-hosting / contributing to the event</p> <p>Online resources - URLs for any related resources</p> <p>Geolocation, latitude and longitude - coordinates of the event location</p>
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
Dataset: Ensemble results comparing L-dependent radial diffusion
<p>Simulation data used in the creation of plots in "Two methods to analyse radial diffusion ensembles: the peril of space- and time- dependent diffusion".</p>
A new method for approximating fractional derivatives/ integrals as a series of higher-integer-order derivatives - examples and results of applying the method to initial/boundary value problems
<p>The posted research data includes examples of the application of the author's fractional derivative/integral approximation method using the sum of higher integer derivatives. The attached text files contain the numerical solutions of the presented examples, recorded as a set of numerical values obtained from the performed computations.</p> <ul> <li>Example 4.1 <br> \(\begin{cases}<br> \displaystyle<br> ^{C}D^{\alpha}_{a+}\sin (x), \\<br> x \in \langle a, 3\pi \rangle \quad \hbox{and} \quad<br> \alpha = \{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},<br> \end{cases}\)<br><br></li> <li>Example 4.2 <br>\( \begin{cases}<br> \displaystyle<br> I^{\alpha}_{0+} e^{-x}\cos 7x, \\<br> x \in \langle 0,1\rangle \quad \hbox{and} \quad<br> \alpha =\{1.0,\ 1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0 \},<br> \end{cases} \)<br><br></li> <li>Example 5.1 <br>\(\begin{cases}<br> ^{C} D_{0+}y(x)+2y(x)=x+ \frac{2x^{\alpha+1}}{\Gamma(\alpha+2)},\\<br> x\in\langle0,1\rangle, \\<br> y(0) = 0; \quad y(1) = \frac{1}{\Gamma(\alpha+2)}, \\<br> \alpha = \{1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0\}. <br>\end{cases}\)<br><br></li> <li>Example 5.2 <br>\(\begin{cases}<br> ^{C}D_{0+}^{\alpha}y(x)+1.8 y(x)=0,\\<br> x\in \langle 0,2\rangle \quad \hbox{and} \quad \alpha=\{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},\\<br> y(0)=1.<br>\end{cases}\)</li> </ul>
Dataset of Survey Results on the Integration of Industry 4.0 in University Education (Baja California, 2024)
<p>This dataset contains the results of a survey conducted in 2024 on the integration of Industry 4.0 concepts and technologies in university education in Baja California. The survey was designed to assess the current state of adoption, challenges, and opportunities related to Industry 4.0 within academic institutions. The data includes responses from engineering students at the Autonomous University of Baja California (UABC) and the Polytechnic University of Baja California (UPBC). The insights gathered aim to inform future strategies for enhancing the implementation of Industry 4.0 in higher education curricula.</p>
Expedited Modeling of Burn Events Results (EMBER) Data Files
<p>This dataset includes photochemical air quality modeling files for simulations of fire impacts on ground-level ozone cocnentrations in the U.S. during the summer of 2023. A data dictionary describes what is included in the each of the files. Detailed information on the model simulations and the file contents is included in a journal article documenting the dataset: Simon, H., Beidler, J., Baker, K.R., Henderson, B.H., Fox, L., Misenis, C., Campbell, P., Vukovich, J. Possiel, N., Eyth, E. Expediated Modeling of Burn Events Results (EMBER): A Screening-Level Dataset of 2023 Ozone Fire Impacts in the US, <em>Data in Brief</em>, https://doi.org/10.1016/j.dib.2024.111208</p> <p>A web-based tool for browsing this dataset is also available at: https://www.epa.gov/air-quality-analysis/expedited-modeling-burn-events-results-ember</p>
Identifying Coronal Mass Ejection Active Region Sources: An automated approach - Catalogue results
<p>Catalogue of Coronal Mass Ejection (CME) active region sources. Includes a database version and a simplified .csv version. For full details, refer to the source code at <a href="https://github.com/JulioHC00/cmesrc">https://github.com/JulioHC00/cmesrc</a>. We include a README file for each describing each column.</p> <p>We also include the raw data used to generate the catalogue so that results may be reproduced following the steps detailed in <a href="https://github.com/JulioHC00/cmesrc">https://github.com/JulioHC00/cmesrc</a>. This is a collection of data from other works and we provide it only to allow the results to be reproduced</p> <p>Below, we detail the data sources for the raw_data folders</p> <p>==============================<br><strong>RAW DATA SOURCES</strong><br>==============================</p> <p><strong>DIMMINGS FOLDER</strong></p> <p>Data is from Solar Demon, .csv was provided by Emil Kraaikamp through private communication.</p> <blockquote> <p>Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images<br>Emil Kraaikamp, Cis Verbeeck<br>J. Space Weather Space Clim. 5 A18 (2015)<br>DOI: 10.1051/swsc/2015019</p> </blockquote> <p><strong>HARPNUM_TO_NOAA FOLDER</strong></p> <p>Obtained from http://jsoc.stanford.edu/doc/data/hmi/harpnum_to_noaa/all_harps_with_noaa_ars.txt</p> <p><strong>LASCO FOLDER</strong></p> <p>This CME catalog is generated and maintained at the CDAW Data Center by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a project of international cooperation between ESA and NASA.</p> <p>Downloaded from https://cdaw.gsfc.nasa.gov/CME_list/</p> <p><strong>MVTS FOLDER</strong></p> <p>Data from</p> <blockquote> <p>Angryk, R.A., Martens, P.C., Aydin, B. et al. Multivariate time series dataset for space weather data analytics. Sci Data 7, 227 (2020). https://doi.org/10.1038/s41597-020-0548-x</p> </blockquote> <p>Available at the Harvard Dataverse</p> <blockquote> <p>Angryk, Rafal; Martens, Petrus; Aydin, Berkay; Kempton, Dustin; Mahajan, Sushant; Basodi, Sunitha; Ahmadzadeh, Azim; Xumin Cai; Filali Boubrahimi, Soukaina; Hamdi, Shah Muhammad; Schuh, Micheal; Georgoulis, Manolis, 2020, "SWAN-SF", https://doi.org/10.7910/DVN/EBCFKM, Harvard Dataverse, V1</p> </blockquote> <p>The DT_SWAN folder contains the same data but with extra columns obtained directly from the Joint Science Operations Center (JSOC) through the python package drms.</p>
Inter-Chemical Correlation results for the study: NHANES20132014 (NHANES Survey 2013-2014)
Title: NHANES Survey 2013-2014 <br>Species: Homo sapiens <br>Number of samples: 10269 <br>Number of named analytes: 218 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20072008 (NHANES Survey 2007-2008)
Title: NHANES Survey 2007-2008 <br>Species: Homo sapiens <br>Number of samples: 8529 <br>Number of named analytes: 174 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20032004 (NHANES Survey 2003-2004)
Title: NHANES Survey 2003-2004 <br>Species: Homo sapiens <br>Number of samples: 10012 <br>Number of named analytes: 427 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES19992000 (NHANES Survey 1999-2000)
Title: NHANES Survey 1999-2000 <br>Species: Homo sapiens <br>Number of samples: 9652 <br>Number of named analytes: 252 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20152016 (NHANES Survey 2015-2016)
Title: NHANES Survey 2015-2016 <br>Species: Homo sapiens <br>Number of samples: 9170 <br>Number of named analytes: 341 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20112012 (NHANES Survey 2011-2012)
Title: NHANES Survey 2011-2012 <br>Species: Homo sapiens <br>Number of samples: 10352 <br>Number of named analytes: 201 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20012002 (NHANES Survey 2001-2002)
Title: NHANES Survey 2001-2002 <br>Species: Homo sapiens <br>Number of samples: 9851 <br>Number of named analytes: 230 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20052006 (NHANES Survey 2005-2006)
Title: NHANES Survey 2005-2006 <br>Species: Homo sapiens <br>Number of samples: 9582 <br>Number of named analytes: 319 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20092010 (NHANES Survey 2009-2010)
Title: NHANES Survey 2009-2010 <br>Species: Homo sapiens <br>Number of samples: 9717 <br>Number of named analytes: 186 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: HHEARx2018-2537 (Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort.)
Title: Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort. <br>Species: Homo sapiens <br>Number of samples: 630 <br>Number of named analytes: 16 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=66 <br>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.